18057691. INCREMENTAL VIDEO HIGHLIGHTS DETECTION SYSTEM AND METHOD simplified abstract (Beijing Zitiao Network Technology Co., Ltd.)
Contents
- 1 INCREMENTAL VIDEO HIGHLIGHTS DETECTION SYSTEM AND METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INCREMENTAL VIDEO HIGHLIGHTS DETECTION SYSTEM AND METHOD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
INCREMENTAL VIDEO HIGHLIGHTS DETECTION SYSTEM AND METHOD
Organization Name
Beijing Zitiao Network Technology Co., Ltd.
Inventor(s)
Xiaojie Jin of Los Angeles CA (US)
INCREMENTAL VIDEO HIGHLIGHTS DETECTION SYSTEM AND METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18057691 titled 'INCREMENTAL VIDEO HIGHLIGHTS DETECTION SYSTEM AND METHOD
Simplified Explanation
The patent application describes a system and method for classifying video frames as highlights based on extracted spatial and temporal features using a convolutional neural network, transformer encoder, and feedforward network model.
- Processor executes video classifying program
- Receives input video and samples video frames
- Extracts frame-wise spatial features using convolutional neural network
- Extracts frame-wise temporal feature for each video frame
- Aggregates spatial and temporal features to provide temporal context
- Inputs aggregated features into transformer encoder for temporal-aware feature representations
- Inputs feature representations into feedforward network model for feedforward-transformed features
- Obtains parameter by comparing feedforward-transformed features to highlight prototypes
- Classifies video frames as highlights based on calculated parameter
Potential Applications
This technology can be applied in various fields such as sports analysis, video editing, content recommendation systems, and surveillance systems.
Problems Solved
This technology helps in automating the process of identifying highlights in videos, saving time and effort for content creators and editors.
Benefits
The system provides accurate and efficient classification of video frames as highlights, improving the overall video viewing experience for users.
Potential Commercial Applications
"Automated Video Highlight Detection Technology: Revolutionizing Content Creation and Editing"
Possible Prior Art
Prior art may include existing video analysis systems that use machine learning algorithms for feature extraction and classification, but may not specifically focus on highlight detection in videos.
What are the potential limitations of this technology in real-world applications?
The technology may face challenges in accurately identifying highlights in videos with complex or dynamic content, leading to potential misclassifications and inaccuracies in the final output.
How does this technology compare to existing video highlight detection methods in terms of accuracy and efficiency?
This technology offers improved accuracy and efficiency in detecting highlights compared to traditional methods, thanks to its advanced feature extraction and classification techniques.
Original Abstract Submitted
Systems and methods are provided that include a processor executing a video classifying program to receive an input video, sample video frames from the input video, extract frame-wise spatial features from the video frames using a convolutional neural network, extract a frame-wise temporal feature for each video frame, aggregate the frame-wise spatial features and the frame-wise temporal feature for each video frame to provide a temporal context to the frame-wise spatial features, input the aggregated frame-wise spatial features and the frame-wise temporal feature for each frame into a transformer encoder to obtain temporal-aware feature representations of the video frames, input the feature representations into a feedforward network model to obtain feedforward-transformed features, obtain a parameter by inputting each feedforward-transformed feature and a set of highlight prototypes into a function comparing the feedforward-transformed features to the set of highlight prototypes, classify the video frames as highlights based on the calculated parameter.
